A Federated and Differentially Private Incentive–Marketing Framework for Privacy-Preserving Cross-Channel Measurement in AI-Powered Digital Commerce

In the U.S. digital economy, small and medium-sized businesses (SMBs) and creators in remote regions face structural disadvantages in access to integrated advertising and incentive platforms, largely due to accelerating privacy regulations and the fragmentation of cross channel datasets. This paper proposes a unified federated and differentially private measurement framework that integrates Topics/Protected Audience, Attribution Reporting, and SKAdNetwork, aiming to achieve privacy-preserving incentive optimization and cross-channel effectiveness measurement for web and mobile environments. The framework prioritizes compliant data usage, resolves data silos across ad ecosystems, and supports privacy-preserving recommendation and incentive allocation. Technically, we design a hybrid architecture that combines federated learning, differential privacy, and low-latency attribution aggregation, while ensuring end-to-end consistency across uplift modeling, multi-touch attribution (MTA), and event-level reporting. Empirical analysis compares the proposed model with state-of-the-art privacy-preserving baselines (e.g., last-touch attribution with DP aggregation), demonstrating substantial gains in accuracy, robustness, and reporting fidelity under strict privacy constraints.